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Five Tales of Random Forest Regression
(Johns Hopkins University, 2016-10-03)
We present a set of variations on the theme of Random Forest regression: two applications to the problem of estimating galactic distances based on photometry which produce results comparable to or better than all other ...
Shape Theoretic and Machine Learning Based Methods for Automatic Clustering and Classification of Cardiomyocytes Based on Action Potential Morphology
(Johns Hopkins University, 2017-10-27)
Stem cells have been a hot topic in the cardiology community for the last decade and a half. Ever since we learned how to differentiate cardiomyocytes from embryonic and induced pluripotent stem cells, there has been a ...
Advances in Systems Science using Network Theory and Machine Learning
(Johns Hopkins University, 2019-02-01)
Systems science is widely used for population, public health, traffic, hazard, and other scientific research. New challenges have come up regarding access to big data as well a deeper consideration of systems complexity. ...
Heterogeneous Chip Multiprocessor: Data Representation, Mixed-Signal Processing Tiles, and System Design
(Johns Hopkins University, 2019-02-01)
With the emergence of big data, the need for more computationally intensive processors that can handle the increased processing demand has risen. Conventional computing paradigms based on the Von Neumann model that separates ...
Geometric Deep Learning for Monocular Object Orientation Estimation
(Johns Hopkins University, 2019-01-03)
Monocular object orientation estimation or estimating the 3D orientation of an object given a single 2D image of the object, is an important component of traditional computer vision problems like scene understanding and ...
Robust Learning Architectures for Perceiving Object Semantics and Geometry
(Johns Hopkins University, 2018-04-11)
Parsing object semantics and geometry in a scene is one core task in visual understanding. This includes classification of object identity and category, localizing and segmenting an object from cluttered background, ...
MACHINE LEARNING AND OPTIMIZATION FOR HEALTHCARE AND ENERGY SYSTEMS
(Johns Hopkins University, 2018-05-11)
Healthcare and energy systems provide critical service to our society. Recent advancement in information technology has enabled these systems to keep retrieving and storing data. In this dissertation, we used machine ...
Generalized Linear Splitting Rules in Decision Forests
(Johns Hopkins University, 2018-03-12)
Random forests (RFs) is one of the most widely employed machine learning algorithms for general classification tasks due to its speed, ease-of-use, and excellent empirical performance. Recent large-scale comparisons of ...
Adaptive Asynchronous Control and Consistency in Distributed Data Exploration Systems
(Johns Hopkins University, 2017-08-29)
Advances in machine learning and streaming systems provide a backbone to transform vast arrays of raw data into valuable information. Leveraging distributed execution, analysis engines can process this information effectively ...
Bias-Variance Tradeoff in a Sliding Window Implementation of the Stochastic Gradient Algorithm
(Johns Hopkins University, 2019-10-23)
This paper provides a framework to analyze stochastic gradient algorithms in a mean squared error (MSE) sense using the asymptotic normality result of the stochastic gradient descent (SGD) iterates. We perform this analysis ...